This (zipped) shapefile is derived from the State Game Land (SGL) vector files provided by the state of Pennsylvania (http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=86). A 1 km buffer was added in QGIS.

For additional information, please see https://zenodo.org/record/4766351

Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. Because beaches in Puerto Rico and the U.S. Virgin Islands are open to the public, beaches also provide important outdoor recreation opportunities for urban residents, so we include beaches as parks in this indicator. Input Data

Southeast Blueprint 2023 subregions: Caribbean
Southeast Blueprint 2023 extent
National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022
Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee Easement
Puerto Rico Protected Natural Areas 2018 (December 2018 update): Terrestrial and marine protected areas (PACAT2018_areas_protegidasPR_TERRESTRES_07052019.shp, PACAT2018_areas_protegidasPR_MARINAS_07052019.shp) 
2020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 census
OpenStreetMap data “multipolygons” layer, accessed 3-14-2023

A polygon from this dataset is considered a park if the “leisure” tag attribute is either “park” or “nature_reserve”, and considered a beach if the value in the “natural” tag attribute is “beach”. OpenStreetMap describes leisure areas as “places people go in their spare time” and natural areas as “a wide variety of physical geography, geological and landcover features”. Data were downloaded in .pbf format and translated ton an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page.

TNC Lands - Public Layer, accessed 3-8-2023
U.S. Virgin Islands beaches layer (separate vector layers for St. Croix, St. Thomas, and St. John) provided by Joe Dwyer with Lynker/the NOAA Caribbean Climate Adaptation Program on 3-3-2023 (contact jdwyer@lynker.com for more information)

Mapping Steps

Most mapping steps were completed using QGIS (v 3.22) Graphical Modeler.
Fix geometry errors in the PAD-US PR data using Fix Geometry. This must be done before any analysis is possible.
Merge the terrestrial PR and VI PAD-US layers.
Use the NOAA coastal relief model to restrict marine parks (marine polygons from PAD-US and Puerto Rico Protected Natural Areas) to areas shallower than 10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature.
Merge into one layer the resulting shallow marine parks from marine PAD-US and the Puerto Rico Protected Natural Areas along with the combined terrestrial PAD-US parks, OpenStreetMap, TNC Lands, and USVI beaches. Omit from the Puerto Rico Protected Areas layer the “Zona de Conservación del Carso”, which has some policy protections and conservation incentives but is not formally protected.
Fix geometry errors in the resulting merged layer using Fix Geometry.
Intersect the resulting fixed file with the Caribbean Blueprint subregion.
Process all multipart polygons to single parts (referred to in Arc software as an “explode”). This helps the indicator capture, as much as possible, the discrete units of a protected area that serve urban residents.
Clip the Census urban area to the Caribbean Blueprint subregion.
Select all polygons that intersect the Census urban extent within 1.2 miles (1,931 m). The 1.2 mi threshold is consistent with the average walking trip on a summer day (U.S. DOT 2002) used to define the walking distance threshold used in the greenways and trails indicator. Note: this is further than the 0.5 mi distance used in the continental version of the indicator. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation.
Dissolve all the park polygons that were selected in the previous step.
Process all multipart polygons to single parts (“explode”) again.
Add a unique ID to the selected parks. This value will be used to join the parks to their buffers.
Create a 1.2 mi (1,931 m) buffer ring around each park using the multiring buffer plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 1.2 mi buffer is created for each park.
Assess the amount of overlap between the buffered park and the Census urban area using overlap analysis. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix. This step creates a table that is joined back to the park polygons using the UniqueID.
Remove parks that had ≤2% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: In the continental version of this indicator, we used a threshold of 10%. In the Caribbean version, we lowered this to 2% in order to capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles.
Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.
Join the buffer attribute table to the previously selected parks, retaining only the parks that exceeded the 2% urban area overlap threshold while buffered. 
Buffer the selected parks by 15 m. Buffering prevents very small parks and narrow beaches from being left out of the indicator when the polygons are converted to raster.
Reclassify the polygons into 7 classes, seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).
Export the final vector file to a shapefile and import to ArcGIS Pro.
Convert the resulting polygons to raster using the ArcPy Polygon to Raster function. Assign values to the pixels in the resulting raster based on the polygon class sizes of the contiguous park areas.
Clip to the Caribbean Blueprint 2023 subregion.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 6 = 75+ acre urban park 5 = >50 to <75 acre urban park 4 = 30 to <50 acre urban park 3 = 10 to <30 acre urban park 2 = 5 to <10 acre urban park 1 = <5 acre urban park 0 = Not identified as an urban park Known Issues

This indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources. 
This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.
This indicator includes parks and beaches from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a park) or incorrect tags (e.g., labelling an area as a park that is not actually a park). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new parks to improve the accuracy and coverage of this indicator in the future.

Other Things to Keep in Mind

This indicator calculates the area of each park using the park polygons from the source data. However, simply converting those park polygons to raster results in some small parks and narrow beaches being left out of the indicator. To capture those areas, we buffered parks and beaches by 15 m and applied the original area calculation to the larger buffered polygon, so as not to inflate the area by including the buffer. As a result, when the buffered polygons are rasterized, the final indicator has some areas of adjacent pixels that receive different scores. While these pixels may appear to be part of one contiguous park or suite of parks, they are scored differently because the park polygons themselves are not actually contiguous. 
The Caribbean version of this indicator uses a slightly different methodology than the continental Southeast version. It includes parks within a 1.2 mi distance from the Census urban area, compared to 0.5 mi in the continental Southeast. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation. Similarly, this indicator uses a 2% threshold of overlap between buffered parks and the Census urban areas, compared to a 10% threshold in the continental Southeast. This helped capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles. Finally, the Caribbean version does not use the impervious surface cutoff applied in the continental Southeast

Urban areas are expanding rapidly, with the majority of the global and US population inhabiting them. Urban forests are critically important for providing ecosystem services to the growing urban populace, but their health is threatened by invasive insects. Insect density and damage are highly variable in different sites across urban landscapes, such that trees in some sites experience outbreaks and are severely damaged while others are relatively unaffected. To protect urban forests against damage from invasive insects and support future delivery of ecosystem services, we must first understand the factors that affect insect density and damage to their hosts across urban landscapes. This study explores how a variety of environmental factors that vary across urban habitats influence density of invasive insects. Specifically, we evaluate how vegetational complexity, distance to buildings, impervious surface, canopy temperature, host availability, and density of co-occurring herbivores impact three invasive pests of elm trees: the elm leaf beetle (Xanthogaleruca luteola), the elm flea weevil (Orchestes steppensis), and the elm leafminer (Fenusa ulmi). Except for building distance, all environmental factors were associated with density of at least one pest species. Furthermore, insect responses to these factors were species-specific, with direction and strength of associations influenced by insect life history. These findings can be used to inform future urban pest management and tree care efforts, making urban forests more resilient in an era where globalization and climate change make them particularly vulnerable to attack. Keywords: urban forest, invasive species, impervious surface, temperature, species interactions. Methods Insect Density At each sampling period, we measured insect density on four branches of each tree, one branch in each cardinal direction (N, S, E, and W). The sampling unit was a 30 cm terminal branch (Dahlsten et al., 1993; Rodrigo et al., 2019), and we assumed equal leaf area per branch. All sampled branches were in the lower canopy up to 3 meters from the ground, and branches that could not be reached from the ground were accessed using a ladder. Sampled branches were haphazardly chosen from a distance where insects were not distinguishable to avoid sampling bias. On each tree branch, we counted individuals of each observable insect stage: beetle eggs, larvae, and adults (the beetle pupates in cryptic locations such as under bark or in the soil, and thus pupae were not counted); weevil leaf mines and adults; and the number of leaves with leafminer mines. Individual leafminer mines were not counted because adult females lay multiple eggs per leaf, and it is common for mines to merge and become indistinguishable from one another as larvae develop. Thus, it was not possible to count the number of individual mines for this species. Leafminer adults were not counted because this stage had disappeared for the season by the start of the first sampling period. The total number of leaves on each branch was also recorded. In addition to serving as the response variable for our environmental hypotheses, insect density of each species was also used as predictor variables for the co-occurring herbivore hypothesis. Tree 0 indicates the end of the dataset. Urban Site Factors Host Availability (AllElm_Density) We measured host availability digitally by counting the number of elm trees within a 100 meter buffer around each tree using QGIS version 3.10.12 (QGIS Development Team, 2022) and a dataset of publicly managed trees provided by municipal forestry departments. We chose a 100 meter radius because significant changes in insect density are detectable for multiple insect species at this spatial scale (Sperry et al., 2001). Although Siberian elm is a preferred host of the insects in this system, other species of elm may also serve as hosts and were thus included in this data set. Following digital assessment, we verified all counts in situ to capture any visible privately owned trees and verify that trees in the dataset were still alive and present in the field. Despite efforts to avoid spatial autocorrelation, four trees had 100 meter buffers that overlapped with the buffer of another tree (that is, two locations where two trees had overlapping buffers). Because the maximum overlap was <14% of the buffer area, we retained these trees in our analyses. Vegetational Complexity (SCI_0_500) We measured the structural complexity of the vegetation in a 10 x 10 meter area around each tree following Shrewsbury & Raupp (2000, 2006). Specifically, we sectioned off a 10 x 10 meter area around each study tree and divided this area into one hundred 12 meter plots. In each of these plots, we recorded five vegetation categories: ground cover (e.g., mulch or turf grass), herbaceous plants (e.g., garden annuals/perennials, tall native grasses), shrubs (e.g., hydrangea, boxwood, barberry), understory trees (e.g., juniper, plum, crabapple, small Siberian Elm), or overstory trees (those with mature canopy including ash, pine, and other elm). One point was awarded for each vegetation type present, resulting in 0-5 points awarded in each plot. To quantify complexity of the vegetation in a continuous way, points were summed for all one hundred plots. Thus, each tree received a vegetational complexity score between 0 and 500. Building Distance (Building Distance_m) To assess the local availability of structures for insect overwintering, we measured the distance of each sampled tree to the nearest building in meters as in Speight et al (1998). This was performed digitally using QGIS version 3.10.12 (QGIS Development Team, 2022) and the ESRI Standard Basemap, which displays built structures. Impervious Surface (ImperviousSurface_20m) Impervious surface data were obtained through the USGS Multi-Resolution Land Characteristics Consortium (Dewitz & US Geological Survey, 2021) on a 30 x 30 meter scale and processed using QGIS version 3.10.12 (QGIS Development Team, 2022). We used the zonal statistics tool to calculate the percentage of impervious surface within a 20 meter buffer surrounding each sampled tree, which is more predictive of herbivorous insect density than impervious surface at larger spatial scales (Just et al., 2019). Although impervious surface data were not available at a smaller spatial scale, the zonal statistics tool allowed us to obtain an estimate of impervious surface within 20 meters of each tree using 30 x 30 meter data by computing an average impervious surface value based on weighted averages of the extent to which each 30 x 30 meter pixel overlapped with the 20 meter buffer around a tree. Canopy Temperature (MeanTemp_Night) Canopy temperature at each tree was measured every 1.5 hours via the iButton Thermochron (model DS1921G-F5). Temperature logging began at 7:30AM MST on June 12 and ended at 7:30AM MST on August 25 for a total of 1,185 data points per logger. We placed each logger in a compostable container to prevent contact with direct sunlight and attached them with a zip tie to branches approximately 2-3 meters from the ground. We placed temperature loggers on the east side of the tree wherever possible or on the west side of the tree if a stable eastern location was not available. Despite efforts to minimize contact with direct sunlight, several loggers recorded artificially inflated temperatures. This made mean and maximum temperatures impractical for analysis. We used mean nighttime temperature in the following analyses (7:30PM-7:30AM MST, n=666 measurements per logger) because the urban heat island effect is less variable, occurs more frequently, and is more intense in urban canopies at night compared to the day (Du et al., 2021; Sun et al., 2019).

This data is for the 1:250000 major disaster risk zoning of the 10km buffer zone on both sides of the permafrost section of the high-grade highway in the Qinghai Tibet region (2023), with a scale of 1:250000. The data format is raster. This data is based on the risk expression given by the United Nations Humanitarian Business Department (1992): Risk=Hazard × Vulnerability, and the risk analysis of disasters in the buffer zone. The risk factors include: elevation, slope, aspect, surface curvature, terrain humidity index, normalized vegetation index, annual average rainfall, distance from rivers, land use, fine-grained soil content, active layer thickness, thawing index, underground ice content, and solar radiation; Vulnerability factors include population density, nighttime light index, gross domestic product, actual livestock production, building distribution, and transportation route distribution. Using GIS analysis, the final results were divided into five categories: very low for extremely low-risk areas, low for low-risk areas, medium for medium risk areas, high for high-risk areas, and very high for extremely high-risk areas. And field verification has been conducted, and the verification results are reliable. Grid data can be opened for mapping using software such as ArcGIS, QGIS, Envi, etc.

Description

This dataset explores threats to New Caledonia's forests, focusing on the probability of deforestation within hotspots of tree community diversity.

The probability of deforestation was derived from the "Spatial scenario of tropical deforestation and carbon emissions for the 21st century" (Vielledent et al., 2023), available as downloadable GeoTIFFs for New Caledonia for the years 2050 and 2100

Hotspots of tree community diversity were identified using the 'Core Forest' class extracted from the dataset 'Classification of New Caledonia Forests According to Edge and Elevation Effects'. Core forest are defined as forest areas located more than 300 meters from the forest edge, characterized by potentially richer tree communities.

To assess habitat quality, each core forest fragment was surrounded by a 500-meter buffer zone. We evaluated forest habitat metrics including forest cover, forest type distribution, and fragmentation index within these buffer zones. Additionally, we analyzed the areas of forest threatened by deforestation in 2050 and 2100. The resulting maps provide decision-making support for stakeholders involved in conserving New Caledonia's forests. The fragmentation index used is the effective mesh size (meff), originally proposed by Jaeger (2000) and updated by Moser and al. (2007) to address boundary effects. A higher index value indicates less fragmented forest.

Content

This dataset was generated, analyzed, and validated using a suite of open-source software tools, including QGIS, PostgreSQL, PostGIS, Python, and the GDAL library, operating on a Linux platform. The compressed file includes six essential files formatted for an ESRI GIS system, utilizing the WGS84 international coordinate system. It is compatible for upload into spatial databases such as PostgreSQL/PostGIS.

Each entry in the attribute table represents a buffer area containing one or several core forests, with the following fields (restricted to 10 characters):

Field Type Description

id_buffer INTEGER Unique identifier for the buffer

area_ha NUMERIC (2 DECIMALS) Area of the buffer in hectares

frag_index NUMERIC Forest fragmentation meff index within the buffer

forest_cov NUMERIC (2 DECIMALS) Proportion of forest within the buffer area (%)

edge_r NUMERIC (2 DECIMALS) Proportion of forest area classified as edge forest (%)

mature_r NUMERIC (2 DECIMALS) Proportion of forest area classified as mature forest (%)

core_r NUMERIC (2 DECIMALS) Proportion of forest area classified as core forest (%)

defor_2050 NUMERIC (2 DECIMALS) Proportion of forest threatened by deforestation in 2050 (%)

defor_2100 NUMERIC (2 DECIMALS) Proportion of forest threatened by deforestation in 2100 (%)

pn BOOLEAN True or False, indicating if the polygon overlaps with the Northern province

ps BOOLEAN True or False, indicating if the polygon overlaps with the Southern province

Limitations

This analysis of forest conservation issues aims to foster dialogue between forest ecologists and forest management strategies in New Caledonia. It should not be applied blindly based solely on the few quantitative variables proposed.

Various scenarios can utilize these metrics, and we urge managers to initially define conservation objectives (such as maximizing biodiversity, reducing fragmentation, restoring disturbed environments, etc.) and consider constraints (such as accessibility, budget, feasibility, etc.) before utilizing this dataset.

While threats was projected for future scenarios, conservation efforts should also prioritize preserving forests in their current state, including addressing increasing fragmentation and biodiversity loss. We encourage users to explore these metrics and their correlation with other quantitative or qualitative variables to gain a deeper understanding of conservation challenges in New Caledonia's forests.

At the onset of the full reopening in Spring 2023 of the Difficult-to-Return Zone of Northeastern Japan following the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident that took place in March 2011, several spatial layers were regrouped and compiled to facilitate environmental studies dealing with the redistribution of radiocesium fallout across landscapes.

The current dataset is composed of 23 shapefiles including those of the delineations of different spatial zones (Intensive Contamination Survey Areas – ICAs, Special Decontamination Zones – SDZ, Difficult-to-Return Zone – DTRZ, and FNDPP location) (Evrard et al. 2019), municipalities where mushroom consumption restrictions were enforced (restricted and partially lifted restrictions), river hydrographic networks and their respective drainage areas (Mano, Niida, Ota, Takase, and Ukedo), dam reservoirs and drainage areas (Mano, Ogaki, Takanokura, and Yokokawa), multiple administrative delineations in Japan (whole Japan administrative boundaries, Prefectures, and municipalities) (GIS, 2016), and one raster file of the reconstruction of initial ¹³⁷Cs fallout across eastern Japan (from Kato et al., 2019).

The current dataset provides a support to a publication submitted to the SOIL journal:

All map processing was carried out using QGIS 3.26.0 (QGIS, 2022) and under the EPSG:WGS 84 projection system.

The ¹³⁷Cs fallout raster (in Bq m^-2, decay-corrected to July 2011) was generated from the point grid of Kato et al. (2019). A total of 126 tiles (0.25 x 0.25 degree) were generated by Inverse Distance Weighted (IDW) interpolation using the 'IDW interpolation' tool with the following settings: distance coefficient P = 1.0 and pixel size (x and y) = 0.0015 degree. Tiles were then merged into a single tile using the raster 'Merge' tool. The initial point grid footprint was manually delineated to define the spatial applicability zone of the airborne survey. A buffer zone corresponding to half plus 10% of the longest distance between two airborne points (x = 0.002, y = 0.003), i.e. 0.0017 degree, was generated using the 'buffer' tool. The single tile was then cut according to the footprint of the buffer zone using the 'clip a raster by a mask layer' tool. A single-band pseudo-colour scale is provided and displays pixels with a value above 1000 Bq m^-2 (eq. global background).

Reason for SelectionProtected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks.Input DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator.Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”.Save merged raster to .tif and import into ArcPro.Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0.Convert the raster produced above to vector using the “RasterToPolygon” tool.Clip to 2024 subregions using “Pairwise Clip” tool.Break apart multipart polygons using “Multipart to single parts” tool.Hand-edit to remove deep marine polygon.Dissolve the resulting data layer.This produces a seamless polygon defining land and shallow marine areas.Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks:All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public.All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use.All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM).All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status.Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes.Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset.The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function.Clip the resulting data layer to the inland and nearshore extent.Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”).Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers.Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster.Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park.Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC).Extract again to the inland and nearshore extent.Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field.Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator.Add color and legend to raster attribute table.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code.Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources.This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly

Behavioral responses of crows to anthropogenic cues for food (Experiment 1 & 2)

Data for Corvus brachrhynchos behavioral responses to food and anthropogenic objects/ cues. Experiments were conducted along an urban-rural gradient in Oneida County, NY, USA.

Contact Andrea Townsend ([aktownse@hamilton.edu] with any questions. This manuscript has been accepted for publication

Merz, M. R., S. Cote, R. Weinberg, T. Malley and A. K. Townsend. "Can I have fries with that? Context-dependent foraging behavior in urban and rural American crows." Behavioral Ecology, Volume 36, Issue 1, January/February 2025, arae098, https://doi.org/10.1093/beheco/arae098

Data files include:

Data files include:

File: experiment1_ImperviousSurface_TrashBin.csv

Variables

Treatment_Type: whether or not a trash can is present (Y/N)

Percent_impervious_50m: The percentage of impervious surface within a 50m radius around each test site...

A WMS (Web Map Service) is a service that produces maps of spatially referenced data starting from geographic information, and is a technical specification defined by the OGC (Open Geospatial Consortium). This WMS features the layer "Deep Aquifer Recharge Areas"; the data was produced in implementation of paragraph 4 of article 24 of the Rules of the Water Protection Plan. Approval of the methodology used and of the limitation on a scale of 1:250,000. This limitation was approved with D.D. 268 of 21 July 2016. The data represents the limits towards the plain of the recharging areas in the strict sense, they have been adapted to the basis of the regional BDTRE (2017 edition) in order to facilitate the implementation of the discipline approved by the Regional Council with Resolution no. 12-6441 of 2 February 2018, by the municipalities. A similar examination was conducted on the upstream limit of the recharging areas in the strict sense in areas where there is no buffer strip. Maps in WMS format can be viewed using various software (e.g. QGis)

This dataset contains images of the rooftops of French buildings, with a large portion of the images from metropolitan France available. Ultimately, it will include around 40,000,000 images, organized by department. This dataset is related to the scientific publication "Thebault, Nerot, Govehovitch, Ménézo - A comprehensive building-wise Residential Photovoltaic system detection in heterogeneous urban and rural areas: application to French territories" Applied Energy, 2025, doi.org/10.1016/j.apenergy.2025.125630 Aerial Land Imagery The aerial imagery used in this study comes from the Institut National de l'Information Géographique et Forestière (IGN), the French national geographic institute. These images are provided in 25 km² RGB tiles with a resolution of 20 cm. The tiles are organized by French department and are freely accessible as JP2 raster files BD Ortho - Institut National Géographique. All the imagery utilized in this project is less than four years old. The availability of department-level imagery is fundamental to our methodology, as both cadastral data processing and the analysis of CNN model predictions are performed at this geographic scale. Building Registry The location and geometry of each building were extracted from a national building registry distributed by the French Etalab project. For each French department, a single SHP file is provided, containing building geometries stored as polygon features. Data Post-Processing Both raster (aerial imagery) and vector (building registry) data were processed using PyQGIS via QGIS. The preprocessing of vector data follows several steps. First, polygons with an area smaller than 10 m² were discarded, as they typically represent small, likely non-residential buildings, which are unlikely to host PV panels. Next, a 4-meter buffer was applied to each polygon to account for the frequent spatial discrepancies between the building registry and the actual building locations. To include additional contextual information in each final image and accommodate these shifts, each polygon was replaced with its oriented rectangular bounding box, minimizing the area of the box. Finally, the X and Y coordinates and a department-based unique identifier were added to each polygon feature. Creation of Building Images Each building polygon was intersected with the corresponding aerial imagery raster to generate a cropped image. These images were saved to individual files. For example, the Herault department (34), one of the more populated regions of France, contains approximately 700,000 images, with an average image size of 120x120 pixels. Notably, 97.9% of these images are smaller than 250x250 pixels. Approximately 1.5% of buildings span multiple raster tiles, resulting in final images that do not fully capture the entire rooftop. Ce jeu de donnée contient les images des toitures des batiments Française, une grande partie des images du territoire métropolitain Français sont disponible. A terme il contiendra environ 40 000 000 images, organisées par départements. Imagerie aérienne L’imagerie aérienne utilisée dans cette étude provient de l’Institut National de l’Information Géographique et Forestière (IGN), l’institut géographique national français. Ces images sont fournies sous forme de tuiles RGB de 25 km² avec une résolution de 20 cm. Les tuiles sont organisées par département français et sont accessibles gratuitement en tant que fichiers raster JP2 (BD Ortho - Institut National Géographique). Toutes les images utilisées dans ce projet ont moins de quatre ans. La disponibilité d’images aériennes à l’échelle départementale est fondamentale pour notre méthodologie, car à la fois le traitement des données cadastrales et l’analyse des prédictions du modèle CNN sont effectués à cette échelle géographique. Registre des bâtiments La localisation et la géométrie de chaque bâtiment ont été extraites d’un registre national des bâtiments distribué par le projet Etalab en France. Pour chaque département français, un fichier SHP unique est fourni, contenant les géométries des bâtiments sous forme de polygones. Post-traitement des données Les fichiers raster (imagerie aérienne) et vectoriels (registre des bâtiments) ont été traités avec PyQGIS via QGIS. Le prétraitement des données vectorielles suit plusieurs étapes. Tout d’abord, les polygones ayant une surface inférieure à 10 m² ont été exclus, car ils représentent généralement des petits bâtiments, probablement non résidentiels, qui sont peu susceptibles d’être équipés de panneaux photovoltaïques. Ensuite, un tampon de 4 mètres a été appliqué à chaque polygone afin de compenser les écarts fréquents entre les données cadastrales et l’emplacement réel des bâtiments. Pour inclure des informations contextuelles supplémentaires dans chaque image finale et tenir compte de ces décalages, chaque polygone a été remplacé par son rectangle englobant orienté, minimisant ainsi la surface du rectangle. Enfin, les coordonnées X...

Concrete habitat: Impervious surface in nest vicinity is associated with avian fitness decline in two urban adapters

We included data and codes used for the analyses presented in the manuscript entitled "Concrete habitat: Impervious surface in nest vicinity is associated with avian fitness decline in two urban adapters" by Michela Corsini and Marta Szulkin.

Description of the Data and file structure

Data are included for each species. The file GT_fled includes data for fledglings analyses in Great tits, BT_fled includes data for fledglings analyses in Blue tits.

The text file GT_fled includes data on the total number of fledglings per breeding event. Nests were checked 25 days after hatching (hatching date was recorded as Day 1) to record ho many ringed birds died.

The R-file entitled "**Fled_analyses_ISA_Corsini2024**" includes all codes for models (glms with quasi-Poisson distribution) and plots (built in ggplot2).

Labels:

Year: Includes the year of the study...

Ice-rich permafrost in the circum-Arctic and sub-Arctic, such as late Pleistocene Yedoma, are especially prone to degradation due to climate change or human activity. When Yedoma deposits thaw, large amounts of frozen organic matter and biogeochemically relevant elements return into current biogeochemical cycles. Building on previous mapping efforts, the objective of this paper is to compile the first digital pan-Arctic Yedoma map and spatial database of Yedoma coverage. Therefore, we 1) synthesized, analyzed, and digitized geological and stratigraphical maps allowing identification of Yedoma occurrence at all available scales, and 2) compiled field data and expert knowledge for creating Yedoma map confidence classes. We used GIS-techniques to vectorize maps and harmonize site information based on expert knowledge. Hence, here we synthesize data on the circum-Arctic and sub-Arctic distribution and thickness of Yedoma for compiling a preliminary circum-polar Yedoma map.

To harmonize the different datasets and to avoid merging artifacts, we applied map edge cleaning while merging data from different database layers. For the digitalization and spatial integration, we used Adobe Photoshop CS6 (Version: 13.0 x64), Adobe Illustrator CS6 (Version 16.0.3 x64), Avenza MAPublisher 9.5.4 (Illustrator Plug-In) and ESRI ArcGIS 10.6.1 for Desktop (Advanced License). Generally, we followed workflow of figure 2 of the related publication (IRYP Version 2, Strauss et al 2021, https://doi.org/10.3389/feart.2021.758360).

We included a range of attributes for Yedoma areas based on lithological and stratigraphic information from the source maps and assigned three different confidence levels of the presence of Yedoma (confirmed, likely, or uncertain). Using a spatial buffer of 20 km around mapped Yedoma occurrences, we derived an extent of the Yedoma domain. Our result is a vector-based map of the current pan-Arctic Yedoma domain that covers approximately 2,587,000 km², whereas Yedoma deposits are found within 480,000 km² of this region. We estimate that 35% of the total Yedoma area today is located in the tundra zone, and 65% in the taiga zone. With this Yedoma mapping, we outlined the substantial spatial extent of late Pleistocene Yedoma deposits and created a unique pan-Arctic dataset including confidence estimates.

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom is also sometimes associated with chemosynthetic communities that form around cold seeps or hydrothermal vents. In these unique ecosystems, micro-organisms that convert chemicals into energy form the base of complex food webs (Love et al. 2013). Hardbottom and associated species provide important habitat structure for many fish and invertebrates (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016).According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Artificial reefs arealso known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019). We did not include active oil and gas structures as human-created hardbottom. Although they provide habitat, because of their temporary nature, risk of contamination, and contributions to climate change, they do not have the same level of conservation value as other artificial structures.Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomusSEABED Gulf of Mexico sediments, accessed 12-14-2023; download the data; view and read more about the data on the National Oceanic and Atmospheric Administration (NOAA) Gulf of Mexico Atlas (select Physical --> Marine geology --> 1. Dominant bottom types and habitats)Bureau of Ocean Energy Management (BOEM) Gulf of Mexico seismic water bottom anomalies, accessed 12-20-2023The Nature Conservancy’s (TNC) South Atlantic Bight Marine Assessment(SABMA); chapter 3 of the final report provides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 12-20-2023 on the NOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023Shipwrecks & artificial reefsNOAA wrecks and obstructions layer, accessed 12-12-2023 on the Marine CadastreLouisiana Department of Wildlife and Fisheries (LDWF) Artificial Reefs: Inshore Artificial Reefs, Nearshore Artificial Reefs, Offshore and Deepwater Artificial Reefs (Google Earth/KML files), accessed 12-19-2023Texas Parks and Wildlife Department (TPWD) Artificial Reefs, accessed 12-19-2023; download the data from The Artificial Reefs Interactive Mapping Application (direct download from interactive mapping application)Mississippi Department of Marine Resources (MDMR) Artificial Reef Bureau: Inshore Reefs, Offshore Reefs, Rigs to Reef (lat/long coordinates), accessed 12-19-2023Alabama Department of Conservation and Natural Resources (ADCNR) Artificial Reefs: Master Alabama Public Reefs v2023 (.xls), accessed 12-19-2023Florida Fish and Wildlife Conservation Commission (FWC):Artificial Reefs in Florida (.xlsx), accessed 12-19-2023Defining inland extent & split with AtlanticMarine Ecoregions Level III from the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023 NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of Mexico), 4 (Central Gulf of Mexico), and 5 (Western Gulf of Mexico), accessed 3-27-2024National Oceanic and Atmospheric Administration (NOAA) Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic; read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data and draft final report entitled Assessment of Benthic Habitats for Fisheries Management provided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Mapping StepsNote: Most of the mapping steps were accomplished using the graphical modeler in QGIS 3.34. Individual models were created to combine data sources and assign ranked values. These models were combined in a single model to assemble all the data sources and create a summary raster.Create a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Gulf version of this indicator to separate it from the Atlantic. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = 'Florida Keys'“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Gulf and Atlantic.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Gulf and Atlantic extent.From the BOEM seismic water bottom anomaly data, extract the following shapefiles: anomaly_confirmed_relic_patchreefs.shp, anomaly_Cretaceous.shp, anomaly_relic_patchreefs.shp, seep_anomaly_confirmed_buried_carbonate.shp, seep_anomaly_confirmed_carbonate.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_positives.shp, seep_anomaly_positives_confirmed_gas.shp, seep_anomaly_positives_confirmed_oil.shp, seep_anomaly_positives_possible_oil.shp, seep_anomaly_confirmed_corals.shp, seep_anomaly_confirmed_hydrate.shp.To create a class of confirmed BOEM features, merge anomaly_confirmed_relic_patchreefs.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_confirmed_corals.shp, and seep_anomaly_confirmed_hydrate.shp and assign a value of 6.To create a class of predicted BOEM features, merge the remaining extracted shapefiles and assign a value of 3.From usSEABED sediments data, use the field “gom_domnc” to extract polygons: rock (dominant and subdominant) receives a value of 2 and gravel (dominant and subdominant) receives a value of 1.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer these locations by 150 m and assign a value of 4. The buffer distance used here, and later for coral locations, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).Merge artificial reef point locations from FL, AL, MS and TX. Buffer these locations by 150 m. Merge this file with the three LA artificial reef polygons and assign a value of 5.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m and assign a value of 7.From the FWC coral and hardbottom dataset polygon locations, fix geometries, reproject to EPSG=5070, then assign coral reefs a value of 7, hardbottom a value of 6, hardbottom with seagrass a value of 6, and probable hardbottom a value of 3. Hand-edit to remove an erroneous hardbottom polygon off of Matagorda Island, TX, resulting from a mistake by Sheridan and Caldwell (2002) when they digitized a DOI sediment map. This error is documented on page 6 of the Gulf of Mexico Fishery Management Council’s 5-Year Review of the Final Generic Amendment Number 3.From the TNC SABMA data, fix geometries and reproject to EPSG=5070, then select all polygons with TEXT_DESC = '01. mapped hard bottom area' and assign a value of 6.Union all of the above vector datasets together—except the vector for class 6 that combines the SABMA and FL data—and assign final indicator values. Class 6 had to be handled separately due to some unexpected GIS processing issues. For overlapping polygons, this value will represent the maximum value at a given location.Clip the unioned polygon dataset to the buffered marine subregions.Convert both the unioned polygon dataset and the separate vector layer for class 6 using GDAL “rasterize”.Fill NoData cells in both rasters with zeroes and, using Extract by Mask, mask the resulting raster with the Gulf indicator extent. Adding zero values helps users better understand the extent of this indicator and to make this indicator layer perform better in online tools.Use the raster calculator to evaluate the maximum value

This ZIP file contains lidar derivatives including a Digital Elevation Map, Slope Analysis, and Hillshade individually encompassing Pennsylvania State Game Lands 251-260 as well as a one kilometer buffer around each region. These files were derived from lidar data provided by the state of Pennsylvania and processed using LAStools and QGIS through Project Kappa (https://zenodo.org/record/4573004). When unzipped, this file is approximately 9.65 GB in size.

For additional information, please see https://zenodo.org/record/4766351.

Questo dataset contiene le aree allagate corredate della stima di metri quadrati e metri cubi di acqua caduti (derivati ​​da DTM) determinati sulla base dei rilievi effettuati dai volontari aderenti all'iniziative di citizen science. Il rilievo avviene utilizzando l'app QField da dispositivo mobile dopo eventi atmosferici particolarmente importanti. Il dataset è generato attraverso uno script Python ( https://github.com/USAGEHub/CS_floods) eseguito all'interno del software QGis partendo da elementi lineari (linestring) semplificati. Ciascuna area viene creata utilizzando l'operazione buffer (sulla base della larghezza indicata dall'utente). Gli elementi rilevati nello stesso giorno e che presentano una sovrapposizione spaziale vengono accorpati. - Il dataset è generato attraverso uno script Python ( https://github.com/USAGEHub/CS_floods) eseguito all'interno del software QGis partendo da elementi linestring semplificati. Ciascuna area viene creata utilizzando l'operazione buffer (sulla base della larghezza indicata dall'utente). Gli elementi rilevati nello stesso giorno e che presentano una sovrapposizione spaziale vengono accorpati. Il dataset contiene geometrie poligonali s con la seguente struttura dati: Giorno: giorno del rilievo; Area_m2: superficie allagata; Q_min: quota minima DTM della superficie allagata misurata in metri; Q_max: quota massima del DTM della superficie allagata misurata in m; Diff_q la profondità stimata del volume allagato in cm; Stima_v: volume d'acqua stimato in metri cubi

An exclusive economic zone (EEZ) is a sea zone prescribed by the United Nations Convention on the Law of the Sea over which a sovereign state has special rights over the exploration and use of marine resources, including energy production from water and wind. The 200 NM zone is measured, country-by-country, from another maritime boundary, the baseline (usually but not in all cases the mean low-water mark, used is not the same thing as the coast line. For each country, the official list of the baseline points is obtained from the United Nations Law of the Sea Maritime Space (http://www.un.org/depts/los/LEGISLATIONANDTREATIES/regionslist.htm).

The data for this layer were obtained from http://www.marineregions.org/eezmethodology.php. The Preferred Citation for this data is Flanders Marine Institute (VLIZ) (2014), Maritime Boundaries Geodatabase, version 8 in conjunction with NOAA. The exclusive economic zone (EEZ) limits and boundaries were created for NOAA's purposes only to update the charted maritime limits and maritime boundaries on NOAA charts and for official depiction, please see NOAA's paper or raster nautical charts (Sourced from NOAA_Version 4.1, 9/10/2013). NOAA provides shapefiles of the Exclusive Economic Zones for different regions of the United States and its overseas territories. In a second phase the database of negotiated treaties from the United Nations Law of the Sea was consulted and imported into a GIS. The geographic coordinates from the documents were converted to decimal degrees and imported into a database. After importing them in ArcGIS, the points were connected by a line. The remaining boundaries were calculated in a GIS in accordance with the United Nations Convention on the Law of the Sea: a 200 Nautical Mile buffer was drawn from the baseline or a median line between 2 countries was calculated.

In questo dataset gli indirizzi residenziali (almeno 1 residente al 31/12/2024) contengono informazioni sulla cosiddetta Regola 3-3-300 (https://nbsi.eu/the-3-30-300-rule/). Per ogni numero civico sono presenti informazioni che riguardano la presenza di chiome arboree nel raggio di 200 metri (regola 30% - https://dati.comune.fe.it/dataset/copertura-alberi) e sulla distanza tra il numero civico e l’area verde pubblica più vicina (https://dati.comune.fe.it/dataset/aree-verdi-istat-2024). - I dati sono ottenuti tramite un workflow eseguito con QGIS Modeler in diversi passaggi: 1) buffer 200 metri intorno a ciascun indirizzo e calcolo della percentuale di canopia arborea; 2) identificazione dei punti di accesso alle aree verdi pubbliche (filtrate); 3) calcolo percorsi dagli indirizzi agli accessi di aree verdi

These ZIP files contains lidar derivatives including a Digital Elevation Map, Slope Analysis, and Hillshade individually encompassing Pennsylvania State Game Lands 12-50 as well as a one kilometer buffer around each region. These files were derived from lidar data provided by the state of Pennsylvania and processed using LAStools and QGIS through Project Kappa (https://zenodo.org/record/4573004). When unzipped, this file is approximately 71.7 GB in size.

For additional information, please see https://zenodo.org/record/4766351.

This ZIP file contains lidar derivatives including a Digital Elevation Map, Slope Analysis, and Hillshade individually encompassing Pennsylvania State Game Lands 111-160 as well as a one kilometer buffer around each region. These files were derived from lidar data provided by the state of Pennsylvania and processed using LAStools and QGIS through Project Kappa (https://zenodo.org/record/4573004). When unzipped, this file is approximately 61.9 GB in size.

For additional information, please see https://zenodo.org/record/4766351.